Umer Arif

Visit my Website umer.ycode.site

Writing #SQL is integral part whether you are developing #webapp or working on #DataScience , sql-practice.com , provides a really practical way to learn from basics or to keep your skills sharp in between projects sql-practice.com

I completed Technical Support Fundamentals! Check out my certificate coursera.org/share/5142e4ca… #Coursera

Quite Informative , watching sql injection discussion on David Bombal #SQL

@MUATH_SWO @Alshaikh2 Welcome for acknowledgement

@UDadabhoy @Alshaikh2 Thank you for your kind sentiments and telling the truth🤝🏻🌹❤️

#سؤال_اليوم : لماذا #السعوديين يحبون حكّامهم أكثر من أي شعب على وجه الأرض على مَرْ العصور ؟

#recursion recursive func always get to the last call, and start evaluating and finalizing from the last one to first one because func call stops the caller from continuing to execute its code and instead makes program execute inside callee. Read more: stackoverflow.com/questions/4602…

recovered .. hope no more spams

@_Miss__Fire_ @Microsoft Lots of congrats

Career Update: Started my first full time job at @Microsoft #SoftwareEngineer #womenintech #Microsoft

#BigData my #2cents : 9 to read small sample of data loops can be a way But #never use them to #read all #chunks instead use pd.concat(textfilereaderobject, ignore_index=True, axis =0) #THIS IS #Fast #Efficient #memory friendly

@keshwewe Share here as well 👋

Apparently, I have covered all the basics of C++ required to start learning Algorithms, Drop a👋 to get my Handwritten Notes. (in pdf format) Retweet, so that a majority of learners get benefitted from this💜

My model scored 0.744 , highest 0.801 #kaggle - kaggle.com/competitions/a…

#BigData my 2cents: #python #DataScience 8-#balance get a #ratio of each #class by #target, #sample as per the #lean #class , n = ratio of lesser occuring class df[df['target'] == higher_occuring_class].sample(frac=n) this will #balance the #examples in #training #data

#BigData my 2cents: 7- hopefully this will speed up fast enough , for further faster #processing further you can use import #modin.pandas as pd this will implement #multithreading , #careful it can slow down many tasks as well, also #some #code #does not like it

#BigData my 2cents: 6 - #memory is very important , #clean no longer required #data #variables, else forever stuck in #vertical #scaling of #hardware import gc import subprocess del dataframe_name gc.collect() if #model_training , #drop fed vars too

#BigData my 2cents: 5-#memory is very important try to find #correlation of each #feature with #target, #drop #insignificant #features #correlation < n #pandas dataframe.corr() handy method to #calculate #correlations, this will reduce data

4. #use sklearn.preprocessing.OrdinalEncoder() , to encode each value inside a columns #use pd.getdummies(dataframe_name , columns = list_of_categorical_columns) , new column for each value, #no_error #cleaner #feature explanation but #dataset #expansion so not always ideal

#BigData my 2cents: 4-#encode #categorical #values #sklearn #label_encoder for #target .replace(val1, with_val1x) only if straight forward like two with 2, #otherwise it will introduce #error C to 1 , D to 2 none higher or lower than the other

#bigdata my 2cents: 3-#clean #data , sometimes #interpolation is better than #dropping #nan #values dataframe.interpolate() dataframe.fillna(some_value or function) dataframe.dropna()